Content Domain 3: Cloud Technology and Services

Task Statement 3.7: Identify AWS artificial intelligence and machine learning (AI/ML) services and analytics services

Let's get started with the seventh task statement covering AWS artificial intelligence, machine learning, and analytics services. And again, let's pause for fundamentals. What is machine learning and artificial intelligence? Machine learning is the science of developing algorithms and statistical models that computer systems use to perform tasks without explicit instructions, relying on patterns and inference instead, to use machine learning algorithms to process large quantities of historical data and identify data patterns. This allows them to predict outcomes more accurately from a given input dataset. For example, data scientists could train a medical application to diagnose cancer from x-ray images by storing millions of scanned images and the corresponding diagnoses. Artificial intelligence is the field of computer science dedicated to solving cognitive problems commonly associated with human intelligence such as learning, problem solving, and pattern recognition. Let's dive a bit deeper. For your workloads in AWS, you can choose from three different levels of machine learning services. Artificial intelligence services, machine learning services, and machine learning frameworks and infrastructure services. For use cases such as anomaly detection, fraud detection, customer churn, and content personalization or recommendation engine. AWS provides a lot of services for this task statement, so take a deeper dive. You should know the different services and the tasks they accomplish. Let's start with the AI services level that provides fully managed services to quickly add machine learning capabilities to your workloads using API calls. This gives you the ability to build powerful, intelligent applications with capabilities such as computer vision, speech, natural language, chatbots, predictions, and recommendations. Services at this level are based on pre-trained or automatically trained machine learning and deep learning models, so you don't need machine learning knowledge to use them. Know these different services at a high-level and how you can use them. You can use Amazon Translate to translate or localize text content, Amazon Polly for text-to-speech conversion, Amazon Lex for building conversational chat bots, and Amazon Rekognition to add image and video analysis to your applications. Dive deeper into all of the AI services. Up next is the machine learning services level that provides managed services and resources for machine learning to developers, data scientists, and researchers. Amazon SageMaker is a focus here and it enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Dive deeper into SageMaker. And a new machine learning service to AWS is Amazon CodeWhisperer, which is a machine learning-powered code generator that provides you with code recommendations in real time and can also scan your code to highlight and define security issues. It probably will not appear on the exam for a few months, but I wanted to include it. Up last is the machine learning framework and Infrastructure. In AWS, you can use open-source machine learning frameworks such as TensorFlow, PyTorch, and Apache MXNet. The Deep Learning AMI and Deep Learning Containers in this level have multiple machine learning frameworks pre-installed that are optimized for performance and are ready to be launched on powerful, machine learning-optimized compute infrastructure, such as Amazon EC2 P3 and P3dn instances, that provides a boost of speed and efficiency to machine learning workloads. Amazon machine learning can create machine learning models based on data stored in S3, Amazon Redshift, or RDS. For example, you can use AI services for sentiment analysis of customer reviews on your retail website, and use managed machine learning services to build a custom model using your own data to predict future sales. Here is an example of a question you may see on your exam. Which AWS service adds visual analysis features to your application to search, verify, and organize potentially millions of images? Well, the first thing you need to know is that we are looking for an AI Services level service. Your first thought might be Amazon SageMaker, but remember that is a machine learning services level service. Amazon Rekognition is the correct answer because it can add images and video analysis to your applications. You provide the image or video to the Rekognition API and then the service can identify objects, people, text, and so on. Let's move on and talk about analytic services. Well, again fundamentals, what is data analytics? Data analytics converts raw data into actionable insights and helps companies gain more visibility and a deeper understanding of their processes and services. It gives them detailed insights into the customer experience and customer problems, and connects insights with actions to create personalized customer experiences, build related digital products, optimize operations, and increase employee productivity. Amazon Athena and Amazon Macie are both AWS analytic services. Athena is an interactive query service that allows you to analyze and query data stored in S3 using standard SQL. Athena is considered to be serverless because you do not have to provision anything, you pay per query or per terabyte scanned. There is no need to set up complex extract, transform, load processes and it works directly with your data stored in S3. Athena is great to query log files, great to generate reports, can analyze your cost and usage reports that are stored in S3, and you can run queries on clickstream data. Now let's check out Macie, and for the exam if you get a question asking for which AWS service helps with Personally Identifiable Information or PII, choose Macie, and I remember it because Macie has an I and so does PII. PII is personal data used to establish an individual's identity, including your name, home address, email address, social security number, drivers license number, passport number, date of birth, bank account information, credit card information, and more. And this is data that needs to be kept secure so it is not used or exploited. Macie is an AWS security service that uses machine learning to discover, classify, and protect sensitive data stored in S3. Macie also uses artificial intelligence to recognize if your S3 objects contain any sensitive PII data and provides dashboards, reports, and alerts and works directly with your data stored in S3. Macie can also analyze your AWS CloudTrail logs. So, for the exam remember, Athena is a query service and Macie is a security service that uses machine learning and artificial intelligence to protect PII. I also want to talk about Athena and Redshift. Athena can query huge datasets in S3, with no preparation and only pay for the data that is queried. Now if you are querying in regular databases, you have to create the table structure and the data structure in advance and this is your schema. So, once you have your schema you have to put data into that schema, and your schema cannot be easily changed after you create it, but with Athena this is not the case. In Athena your data stays in S3 in whatever format it is put in and Athena also supports other data formats, like Parquet, XML, JSON, CSV, and more. Now for Amazon Redshift. Remember Redshift is a petabyte scale data warehousing solution form AWS, and it is a column-based database engine for analytical workloads that is designed for Online Analytical Processing. Redshift uses a cluster architecture, and unloads and uploads data to S3. It can also accept data from DynamoDB, DMS, and other databases, and Amazon Kinesis too. And to go along with S3, we can use Redshift Spectrum to perform queries directly against S3. This is not like Athena, with Redshift Spectrum you must have a Redshift cluster, but you do not need to load the data into Redshift. You can simply query directly from S3. Athena is serverless so there is not an instance to provision. We just mentioned Amazon Kinesis. Let's cover that next. Kinesis processes and analyzes streaming data at any scale as a fully managed service. With Kinesis, you can ingest real-time data, such as video, audio, application logs, website clickstreams, and IoT data, for machine learning, analytics, and other applications. Real time is a keyword for Kinesis. An example of this could be a mobile application that tracks and streams what users tap on and which areas of that application are most used. All of this is streamed and when you only had a few hundred users this stream of data is easy to handle, but what happens when that jumps to millions of users? Well, this is when you could use Kinesis to ingest huge quantities of data in real time and remember it is a fully managed scalable service. Dive deeper into the Kinesis family for more services like Kinesis Data Firehose and Kinesis Data Analytics. Let's also talk about AWS Glue because preparing your data to obtain quality results is the first step in an analytics or machine learning project. AWS Glue is a serverless data integration service that makes it easy for analytics users to discover, prepare, move, and integrate data from multiple sources. You can use it for analytics, machine learning, and application development. It also includes additional productivity and data operations tooling for authoring, running jobs, and implementing business workflows. You can visually create, run, and monitor extract, transform, and load pipelines to load data into your data lakes. Also, you can immediately search and query cataloged data using Amazon Athena, or Amazon Elastic Map Reduce, and also Amazon Redshift Spectrum. Another AWS service of focus is Amazon QuickSight which is a fast, business intelligence service that delivers insights to everyone in your organization. As a fully managed service, Amazon QuickSight lets you create and publish interactive dashboards that include machine learning insights. Here is a question. Which AWS service supports business intelligence tools such as Apache Spark to perform data transformation workloads and analytics? Well, we just mentioned QuickSight with business intelligence. Is that the correct answer? No. What about Amazon OpenSearch? Also no. Amazon OpenSearch is incorrect because it is a type of database and search engine, and it does not support Apache business intelligence tools and you cannot perform extract, transform, load jobs using these tools alone. But EMR is a web service that enables businesses, researchers, data analysts, and developers to process vast amounts of data. It utilizes a hosted Apache Hadoop framework running on the infrastructure of Amazon EC2 and Amazon S3. EMR can securely and reliably handle broad sets of big data use cases, including machine learning, data transformations, financial and scientific simulation, bioinformatics, log analysis, and deep learning too. Let's get started with the last task statement and talk about other AWS services that are in-scope for this exam.